Generation of a virtual three-dimensional model of a hydrocarbon reservoir

ABSTRACT

A computer system generates a virtual three-dimensional (3D) model of a hydrocarbon reservoir using a machine learning algorithm. The machine learning algorithm is trained using information obtained from multiple hydrocarbon wells. The virtual 3D model includes a reservoir pressure model of the hydrocarbon reservoir indicating variations in reservoir pressure in accordance with time. A fluid saturation model of the hydrocarbon reservoir indicates variations in reservoir saturation in accordance with time. The computer system executes the machine learning algorithm to determine the variations in the reservoir pressure and the variations in the reservoir saturation with respect to the multiple hydrocarbon wells based on the virtual 3D model. A display device of the computer system generates a graphical representation of the variations in the reservoir pressure and the variations in the reservoir saturation in accordance with time.

TECHNICAL FIELD

This description relates generally to hydrocarbon wells, for example, togeneration of a reservoir digital twin using machine learning.

BACKGROUND

Hydrocarbon recovery from oil wells poses increasing challenges as aresult of inadequate data logging across different years and changes inhydrocarbon well dynamics. Traditional methods based on the use ofnumerical simulation can lead to inefficient results for predictingchanges in key hydrocarbon reservoir parameters.

SUMMARY

Methods for generating a virtual three-dimensional (3D) model of ahydrocarbon reservoir include using a machine learning algorithm. Themachine learning algorithm is trained using information obtained frommultiple hydrocarbon wells. The virtual 3D model includes a reservoirpressure model of the hydrocarbon reservoir indicating variations inreservoir pressure in accordance with time. A fluid saturation model ofthe hydrocarbon reservoir indicates variations in reservoir saturationin accordance with time. The computer system executes the machinelearning algorithm to determine the variations in the reservoir pressureand the variations in the reservoir saturation with respect to themultiple hydrocarbon wells based on the virtual 3D model. A displaydevice of the computer system generates a graphical representation ofthe variations in the reservoir pressure and the variations in thereservoir saturation in accordance with time.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an architecture for generation of a reservoir digitaltwin using machine learning.

FIG. 2 illustrates a process for training a machine learning algorithm.

FIG. 3 illustrates an example vertical production profile.

FIG. 4 illustrates an example machine.

FIG. 5 illustrates a process for generation of a virtualthree-dimensional model of a hydrocarbon reservoir.

DETAILED DESCRIPTION

The implementations disclosed provide methods, apparatus, and systemsfor generation of a reservoir digital twin using machine learning. Theimplementations create a three-dimensional (3D) digital replica of ahydrocarbon reservoir using artificial intelligence to predict changesin key dynamic reservoir parameters (such as dynamic saturation andpressure) between hydrocarbon wells. Further, the implementations enablethe prediction of more efficient production and injection strategiesacross the reservoir surveillance program compared to traditionalmethods. The predictions are made independently of the numericalsimulation model. The predictions are based on training a machinelearning algorithm using static and dynamic data that includesproduction and injection data, saturation and production logs, reservoirpressure surveillance, petrophysical data, and a geology model.

Among other benefits and advantages, the methods provide a flexible andintegrated framework for generation of a reservoir digital twin usingmachine learning. The implementations disclosed use artificialintelligence to predict changes in reservoir pressure and saturationparameters and reservoir performance during the life cycle of thereservoir, independent of traditional simulation models. Theimplementations thus generate new hydrocarbon extraction opportunitiesbeyond traditional models. Saturation logs and vertical production logsare normalized against time and are used directly as inputs by thedisclosed implementations, while traditional methods that depend onnumerical simulation models use such data for only calibration. Hence,the accuracy of the reservoir digital twin model is improved compared totraditional methods. Because the implementations normalize thesaturation logs in accordance with time, the implementations provide formore-accurate mapping of the physical reservoir to the digital twincompared to traditional methods.

FIG. 1 illustrates an architecture for generation of a reservoir digitaltwin using machine learning, in accordance with one or moreimplementations. An oil reservoir or hydrocarbon reservoir refers to asubsurface pool of hydrocarbons contained in porous or fractured rockformations. A hydrocarbon well refers to a boring in the Earth that isdesigned to bring petroleum oil hydrocarbons and natural gas to thesurface. Multiple hydrocarbon wells can be bored in a reservoir. Thereservoir digital twin refers to a 3D digital replica of a reservoir.The architecture illustrated in FIG. 1 bridges the physical and thevirtual world, such that data can be transmitted seamlessly allowing thereservoir digital twin to simultaneously model the physical reservoir.In some implementations, the architecture illustrated in FIG. 1 isprocessed by the computer system illustrated and described in moredetail with reference to FIG. 4.

The computer system receives information obtained from a hydrocarbonreservoir. The hydrocarbon reservoir is associated with multiplehydrocarbon wells. The information obtained from the hydrocarbonreservoir includes the reservoir porosity logs 100 and reservoirsaturation logs 110 for the hydrocarbon reservoir. The reservoirporosity logs 100 and the reservoir saturation logs 110 can be derivedfrom well logging tools in a wellbore during the drilling process. Awellbore is a drilling to aid in the exploration and recovery of naturalresources including oil, gas or water. The information obtained from thehydrocarbon reservoir further includes petrophysical data 104 and rocktyping data 122. The petrophysical data 104 can be used indeterminations of lithology, porosity, water saturation, andpermeability at the reservoir level. The rock typing data 122 is used toassign reservoir properties to geological facies across the oilfield.

The information obtained from the hydrocarbon reservoir further includespressure transient test results 124 and vertical production logs 102.The pressure transient test results 124 are a source of the dynamicreservoir data and reflect data obtained from tests on oil and gas wellsat different stages of the drilling, completion, and productionprocesses. The vertical production logs 102 reflect downhole flowingproduction profiles, identify zonal contributions of water, oil, andgas, and can be used to validate and calibrate the reservoir modeling.The information obtained from the hydrocarbon reservoir further includesreservoir pressure logs 108 and reservoir saturation logs 110. Thereservoir pressure logs 108 include the static pressure, pressure-depthplots, or the average reservoir pressure across different points intime. The reservoir saturation logs 110 include saturation monitoringlogs run at different times according to different logging frequencies.The information obtained from the hydrocarbon reservoir further includesproduction performance 128 and injection performance 130. The productionperformance 128 includes production rates of the hydrocarbons while theinjection performance 130 includes flow rates of the water or brine usedfor injecting a hydrocarbon well, bottomhole pressure, and otherinjection parameters.

A machine learning algorithm of the computer system generates a virtualthree-dimensional (3D) model 112 of the hydrocarbon reservoir based onthe information obtained from the hydrocarbon reservoir. In someimplementations, the machine learning algorithm is part of an IndustrialRevolution 4.0 (IR 4.0) framework that is augmented with wirelessconnectivity and sensors, and connected to a system that can visualizethe entire production line using artificial intelligence for decisionmaking. In some implementations, the machine learning algorithm is partof a framework using artificial intelligence, automation and dataexchange in manufacturing technologies and processes, includingcyber-physical systems (CPS), the internet of things (IoT), industrialinternet of things (IIOT), cloud computing, and cognitive computing. Forexample, the machine learning algorithm can use unsupervised learning(such as K-Means Clustering) or extract features of interest from theinformation obtained from the hydrocarbon reservoir. In someimplementations, artificial neural networks, gradient boosting, orsupport vectors are used.

The virtual 3D model 112 is sometimes referred to as a digital twinreplica or a reservoir digital twin. The virtual 3D model 112 is a 3Dsub-surface model of the hydrocarbon reservoir undergoing waterinjection. Artificial intelligence is used to predict variations in thereservoir saturation 136 based on normalizing the data in the reservoirsaturation logs 110 with respect to time for mapping the physicalparameters of the hydrocarbon reservoir to the virtual 3D model 112. Thevirtual 3D model 112 enables reservoir management decisions to be madein less time to increase the efficiency of the production and injectionstrategy compared to traditional methods. The locations and requirementsof infill wells for improving the field performance and reservoir sweepare determined from the virtual 3D model 112.

In some implementations, the machine learning algorithm reducesredundancy in the training data (the received information obtained fromthe hydrocarbon reservoir) by transforming the training data into areduced set of features (a feature vector). The feature vector containsthe relevant information from the training data, such that features ofinterest are identified by the machine learning algorithm using thereduced representation instead of the complete training data. In someimplementations, the feature vector includes information characterizingkey indicators (dynamic data) in the performance of the hydrocarbonreservoir, such as changes in the saturation logs 110, production logs102, vertical production profile 140, injection performance 130,pressure data 108, and pressure transient tests 124.

In some implementations, the machine learning training and executionmethods described combine the use of unsupervised and supervisedmethods. For example, the machine learning algorithm can begin thetraining process using unsupervised method for clustering of hydrocarbonwells based on production performance 128, pressure surveys (reservoirpressure logs 108 obtained from a reservoir pressure surveillanceprogram), and pressure transient test results 124. The machine learningalgorithm then applies a supervised method per cluster to train theunderlying machine learning models and enable the prediction of keyreservoir parameters, such as the change in oil saturation 136 andpressure 114 with time. Such a combination of the use of unsupervisedand supervised methods addresses the implicitly heterogenic nature ofreservoir properties resulting from the presence of geological featuresand fluids properties that affect fluid flow at the reservoir scale andwater breakthrough timing at hydrocarbon wells.

The virtual 3D model 112 includes a dynamic permeability model 126 ofthe hydrocarbon reservoir indicating variations in reservoirpermeability 138 in accordance with a depth from a surface of the Earth.Permeability is a characteristic of a formation or a zone that is usedto estimate the flow rate. Permeability is a property of a rock anddetermined in units of millidarcy (mD). The dynamic permeability model126 is referred to as being dynamic because the permeability isdetermined with respect to the depth within the hydrocarbon reservoirusing pressure transient tests and production logs from PLTs.

In some implementations, the computer system trains the machine learningalgorithm based on the pressure transient test results 124 and avertical production profile 140 of the hydrocarbon reservoir. Thevertical production profile 140 represents the fluid movement in ahydrocarbon well, a composition of the liquid and gas, the temperatureof the fluid filling the well, a profile of the pressure gradient, andthe amount of water in the fluid. In some implementations, the computersystem trains the machine learning algorithm by extracting the verticalproduction profile 140 from the vertical production logs 102. Thevertical production profile 140 is extracted from the verticalproduction logs 102 using direct measurements by running logs into theborehole and across the hydrocarbon reservoir of interest. The flow rateis measured with respect to the depth from the surface of the earth. Theresults are determined as a percentage contribution of each depthinterval across the hydrocarbon reservoir. The vertical productionprofile 140 is extracted, such that the reservoir permeabilitycorresponds to the depth from a surface of the Earth. An examplevertical production profile 140 is illustrated and described in moredetail with reference to FIG. 3.

The trained machine learning algorithm generates the dynamicpermeability model 126 of the hydrocarbon reservoir based on thepressure transient test results 124 and the vertical production profile140. For example, a porosity model is constructed using the porositylogs 100, which include Neutron logs and density logs. The dynamicpermeability model 126 is generated using dynamic data from the receivedinformation obtained from the hydrocarbon reservoir. For example, themachine learning algorithm generates the dynamic permeability model 126using data obtained from productivity index (PI) tests and one-phaseproduction logging tools (PLTs). The PI tests include measurements ofinitial or average reservoir pressure and a measurement of flow rate andflowing bottomhole pressure at stabilized producing conditions. The PIrepresents well productivity and wellbore conditions during the life ofa hydrocarbon well. The PI tests are also used to estimate formationpermeability. The machine learning model can distribute the reservoirpermeability in accordance with depth according to the productiondown-hole profile under an assumption that the total permeabilityestimated by the PI tests is linearly related to the vertical productionprofile 140 obtained from the PLTs.

The virtual 3D model includes a dynamic reservoir pressure model 132 ofthe hydrocarbon reservoir indicating variations in the reservoirpressure 114 in accordance with time. In some implementations, thecomputer system trains the machine learning algorithm using thereservoir pressure logs 108, the production performance 128, and theinjection performance 130. For example, an areal pressure distributionand a vertical pressure distribution are determined using the reservoirpressure logs 108 obtained from a reservoir pressure surveillanceprogram over time. The areal pressure distribution refers to thepressure distribution across an area.

The hydrocarbon reservoir includes multiple formations. The dynamicreservoir pressure model 132 indicates a vertical reservoir pressuregradient across the multiple formations. The machine learning algorithmis trained to predict the vertical reservoir pressure gradient acrossthe formations. The machine learning algorithm assumes that theformations are in hydraulic vertical communication. A fixed pressuregradient is assumed to distribute pressure vertically with depth. Theresulting pressure is directly related to the injection performance 130and the production performance 128 in the field, assuming a weakaquifer. A weak aquifer is one in which the water recharge rate is lessthan the reservoir's fluid withdrawal rate. Such reservoirs are alsoreferred to as partial waterdrives and they are characterized bypressure declines greater than a complete waterdrive but less than avolumetric reservoir. The reservoir pressure variations 114 from thedynamic reservoir pressure model 132 are predicted by training themachine learning algorithm using the production data 128 and theinjection data 130 with historical pressure measurements. The machinelearning algorithm generates the dynamic reservoir pressure model 132based on the training data.

The virtual 3D model 112 includes a dynamic fluid saturation model 134of the hydrocarbon reservoir indicating variations in the reservoirsaturation 136 in accordance with time. The machine learning algorithmis trained to provide variations in the reservoir saturation 136 of thehydrocarbon reservoir in accordance with time. The machine learningalgorithm is trained using the reservoir saturation logs 110, thevertical production logs 102, the production performance 128, theinjection performance 130, the reservoir pressure logs 108, thepetrophysical data 104, and the rock typing data 122. The reservoirsaturation logs 110 provide the distribution of the vertical and arealinitial saturation. Predictions of saturation change with time isgenerated by the machine learning model using training data includingthe field-subsurface observed data at the hydrocarbon well level. Thetraining data includes the time lapse with respect to the reservoirsaturation logs 110, the time lapse with respect to the PLTs, theproduction performance 128, the injection performance 130, the reservoirpressure logs 108, and the rock typing data 122. In someimplementations, the computer system normalizes the reservoir saturationlogs 120 in accordance with time to provide the variations in reservoirsaturation 136. The prediction capabilities of the virtual 3D model 112,achieved by artificial intelligence methods, are utilized to predict theamount of saturation in hydrocarbon wells that were not logged at thetime of interest for mapping the oil saturation.

In some implementations, the computer system transforms the informationobtained from the hydrocarbon reservoir using a variogram in accordancewith the variations in the reservoir saturation 136. In otherimplementations, the machine learning algorithm is used to perform thetransformation. In some implementations the variogram is a functiondescribing the degree of spatial dependence of a spatial random field ora stochastic process. For example, a statistical method is adapted todescribe graphically the spatial continuity of the porosity andpermeability between the hydrocarbon wells. The computer system of FIG.4 executes the machine learning algorithm to determine the variations inthe reservoir pressure 114 and the variations in the reservoirsaturation 136 with respect to the multiple hydrocarbon wells based onthe virtual 3D model 112.

The machine learning algorithm transforms the reservoir saturation logs110, the vertical production logs 102, the production performance 128,the injection performance 130, the reservoir pressure logs 108, thepetrophysical data 104, and the rock typing data 122 using thevariogram. For example, the information obtained from the hydrocarbonreservoir is mapped using a variogram statistical method for 3D modelingwhile generating the dynamic permeability model 126 and mapping changesin the reservoir saturation with time between the hydrocarbon wells. Thepredictions of reservoir saturation enables time-normalization of thedata for mapping. The time-normalization is useful because logginghydrocarbon wells at a single frequency is expensive. The machinelearning algorithm further determines the variations in the reservoirpermeability 138, the variations in the reservoir pressure 114, and thevariations in the reservoir saturation 136 with respect to the multiplehydrocarbon wells based on the virtual 3D model 112.

A display device (for example, the display device 424 illustrated inFIG. 4) of the computer system generates a graphical representation ofthe variations in the reservoir permeability 138, the variations in thereservoir pressure 114, and the variations in the reservoir saturation136 in accordance with time. The display device 424 is illustrated anddescribed in more detail with reference to FIG. 4. The graphicalrepresentation portrays the variations in the reservoir permeability138, the variations in the reservoir pressure 114, and the variations inthe reservoir saturation 136 in accordance with time. The graphicalrepresentation can include text, pie charts, bar graphs, and numericalvalues.

In some implementations, the computer system determines locations ofinfill drillings and a logging frequency 118 based on the virtual 3Dmodel 112. Based on a magnitude of a difference between the predicteddynamic reservoir saturation 136 and observed data, the loggingfrequency 118 and an annual surveillance program for the oilfield isimproved compared to traditional methods. The computer system determineslocations of infill drillings by the generated graphical maps of thepredicted remaining saturation and pressure at a future time to targetareas characterized by increased pressure and a slow change insaturation relative to other areas.

The computer system determines a logging frequency 118 by uncertaintybound analysis to determine a gap between the measured and predicteddata. A greater bound suggests that the logging frequency 118 should beincreased for the targeted hydrocarbon well or areas. On the other hand,a lesser bound suggests that the logging frequency 118 should bedecreased. The locations of the infill drilling and the loggingfrequency 118 are associated with an increase in a sweep efficiency ofthe hydrocarbon reservoir compared to traditional methods. The reservoirsweep efficiency refers to the effectiveness of the hydrocarbonrecovery, depending on the volume of the hydrocarbon reservoir contactedby the injection fluids. The reservoir sweep efficiency depends on theinjection program, fractures in the hydrocarbon reservoir, the positionsof hydrocarbon-water contact, the thickness of the hydrocarbonreservoir, the reservoir permeability, a difference in density betweenthe injection fluids and the hydrocarbons, and the flow rate.

The computer system further determines a production strategy 116 basedon the virtual 3D model 112 having greater efficiency compared totraditional methods. In some implementations, the improved productionstrategy 116 generates new extraction opportunities beyond results fromnumerical simulations to increase the reservoir sweep efficiency and theultimate hydrocarbon recovery with more efficient planning of the infilldrillings. In some implementations, the improved production strategy 116addresses the complexity of analyzing increased amounts of historicaldata with increased efficiency and accuracy compared to traditionalmethods. In some implementations, the improved production strategy 116reduces bias in data modeling, interpretation, and decision making formore efficient hydrocarbon extraction compared to traditional methods.In some implementations, the improved production strategy 116 providesnew optimization insights for reservoir surveillance compared totraditional methods, such as numerical simulation. In someimplementations, the improved production strategy 116 enables preventiveactions based on timely predictions. In some implementations, theimproved production strategy 116 reduces the over-engineering of simpletasks to render accurate and intelligent decisions with improvedengineering efficiency compared to numerical simulation.

FIG. 2 illustrates a process for training a machine learning algorithm.The machine learning algorithm is used for generation of a reservoirdigital twin (for example, the virtual 3D model 112 illustrated in FIG.1). In some implementations the process of FIG. 2 is performed by thecomputer system illustrated and described in more detail with referenceto FIG. 4.

The computer system receives 204 information obtained from a hydrocarbonreservoir. The hydrocarbon reservoir is associated with multiplehydrocarbon wells. The information includes porosity logs 100,petrophysical data 104, rock typing data 122, pressure transient testresults 124, vertical production logs 102, reservoir pressure logs 108,reservoir saturation logs 110, production performance 128, and injectionperformance 130.

The computer system normalizes 208 the reservoir saturation logs 110 inaccordance with time. Artificial intelligence can be used to predictvariations in the reservoir saturation 136 based on normalizing the datain the reservoir saturation logs 110 with respect to time for mappingthe physical parameters of the hydrocarbon reservoir to the virtual 3Dmodel 112. The virtual 3D model 112 is illustrated and described in moredetail with reference to FIG. 1.

The computer system trains 212 a machine learning algorithm to providevariations in reservoir saturation 136 of the hydrocarbon reservoir inaccordance with time. The machine learning algorithm is trained usingthe reservoir saturation logs 110, the vertical production logs 102, theproduction performance 128, the injection performance 130, the reservoirpressure logs 108, the petrophysical data 104, and the rock typing data122.

The computer system trains 216 the machine learning algorithm using thereservoir pressure logs 108, the production performance 128, and theinjection performance 130 to provide variations in reservoir pressure114 of the hydrocarbon reservoir in accordance with time. For example,an areal pressure distribution and a vertical pressure distribution aredetermined using the reservoir pressure logs 108 obtained from areservoir pressure surveillance program over time. The areal pressuredistribution refers to the pressure distribution across an area. In someimplementations, the computer system trains the machine learningalgorithm using any one or more of the following methods: independentcomponent analysis, Isomap, Kernel PCA, latent semantic analysis,partial least squares, principal component analysis, multifactordimensionality reduction, nonlinear dimensionality reduction,multilinear principal component analysis, multilinear subspace learning,semidefinite embedding, Autoencoder, and deep feature synthesis.

A display device (for example, the display device 424 in FIG. 4) of thecomputer system generates 220 a graphical representation of thevariations in the reservoir permeability 138, the variations in thereservoir pressure 114, and the variations in the reservoir saturation136 in accordance with time. The display device 424 is illustrated anddescribed in more detail with reference to FIG. 4. The graphicalrepresentation portrays the variations in the reservoir permeability138, the variations in the reservoir pressure 114, and the variations inthe reservoir saturation 136 in accordance with time. The graphicalrepresentation can include text, pie charts, bar graphs, and numericalvalues.

FIG. 3 illustrates an example vertical production profile 140. Thevertical production profile 140 is obtained using the verticalproduction logs 102 obtained from PLTs. The vertical production logs 102measure the flow rate in accordance with the depth indicating zoneswithin the reservoir in addition to identifying water and oil entries.The vertical production profile 140 is sometimes referred to as aformation signature through which the ability of the different zones toflow oil, water, or both is defined relative to each other.

FIG. 4 is a block diagram of an example computer system used to providecomputational functionalities associated with described algorithms,methods, functions, processes, flows, and procedures described in thepresent disclosure, according to some implementations of the presentdisclosure. The illustrated computer 402 is intended to encompass anycomputing device such as a server, a desktop computer, a laptop ornotebook computer, a wireless data port, a smart phone, a personal dataassistant (PDA), a tablet computing device, or one or more processorswithin these devices, including physical instances, virtual instances,or both. The computer 402 can include input devices such as keypads,keyboards, and touch screens that can accept user information. Also, thecomputer 402 can include output display devices 424 that can conveyinformation associated with the operation of the computer 402. Theinformation can include digital data, visual data, audio information, ora combination of information. The information can be presented in agraphical user interface (GUI).

The computer 402 can serve in a role as a client, a network component, aserver, a database, a persistency, or components of a computer systemfor performing the subject matter described in the present disclosure.The illustrated computer 402 is communicably coupled with a network 430.In some implementations, one or more components of the computer 402 canbe configured to operate within different environments, includingcloud-computing-based environments, local environments, globalenvironments, and combinations of environments.

At a top level, the computer 402 is an electronic computing deviceoperable to receive, transmit, process, store, and manage data andinformation associated with the described subject matter. According tosome implementations, the computer 402 can also include, or becommunicably coupled with, an application server, an email server, a webserver, a caching server, a streaming data server, or a combination ofservers.

The computer 402 can receive requests over network 430 from a clientapplication (for example, executing on another computer 402). Thecomputer 402 can respond to the received requests by processing thereceived requests using software applications. Requests can also be sentto the computer 402 from internal users (for example, from a commandconsole), external (or third) parties, automated applications, entities,individuals, systems, and computers.

Each of the components of the computer 402 can communicate using asystem bus 403. In some implementations, any or all of the components ofthe computer 402, including hardware or software components, caninterface with each other or the interface 304 (or a combination ofboth) over the system bus 403. Interfaces can use an applicationprogramming interface (API) 412, a service layer 413, or a combinationof the API 412 and service layer 413. The API 412 can includespecifications for routines, data structures, and object classes. TheAPI 412 can be either computer-language independent or dependent. TheAPI 412 can refer to a complete interface, a single function, or a setof APIs.

The service layer 413 can provide software services to the computer 402and other components (whether illustrated or not) that are communicablycoupled to the computer 402. The functionality of the computer 402 canbe accessible for all service consumers using this service layer.Software services, such as those provided by the service layer 413, canprovide reusable, defined functionalities through a defined interface.For example, the interface can be software written in JAVA, C++, or alanguage providing data in extensible markup language (XML) format.While illustrated as an integrated component of the computer 402, inalternative implementations, the API 412 or the service layer 413 can bestand-alone components in relation to other components of the computer402 and other components communicably coupled to the computer 402.Moreover, any or all parts of the API 412 or the service layer 413 canbe implemented as child or sub-modules of another software module,enterprise application, or hardware module without departing from thescope of the present disclosure.

The computer 402 includes an interface 404. Although illustrated as asingle interface 404 in FIG. 4, two or more interfaces 404 can be usedaccording to particular needs, desires, or particular implementations ofthe computer 402 and the described functionality. The interface 404 canbe used by the computer 402 for communicating with other systems thatare connected to the network 430 (whether illustrated or not) in adistributed environment. Generally, the interface 404 can include, or beimplemented using, logic encoded in software or hardware (or acombination of software and hardware) operable to communicate with thenetwork 430. More specifically, the interface 404 can include softwaresupporting one or more communication protocols associated withcommunications. As such, the network 430 or the interface's hardware canbe operable to communicate physical signals within and outside of theillustrated computer 402.

The computer 402 includes a processor 405. Although illustrated as asingle processor 405 in FIG. 4, two or more processors 405 can be usedaccording to particular needs, desires, or particular implementations ofthe computer 402 and the described functionality. Generally, theprocessor 405 can execute instructions and can manipulate data toperform the operations of the computer 402, including operations usingalgorithms, methods, functions, processes, flows, and procedures asdescribed in the present disclosure.

The computer 402 also includes a database 406 that can hold data for thecomputer 402 and other components connected to the network 430 (whetherillustrated or not). For example, database 406 can be an in-memory,conventional, or a database storing data consistent with the presentdisclosure. In some implementations, database 406 can be a combinationof two or more different database types (for example, hybrid in-memoryand conventional databases) according to particular needs, desires, orparticular implementations of the computer 402 and the describedfunctionality. Although illustrated as a single database 406 in FIG. 4,two or more databases (of the same, different, or combination of types)can be used according to particular needs, desires, or particularimplementations of the computer 402 and the described functionality.While database 406 is illustrated as an internal component of thecomputer 402, in alternative implementations, database 406 can beexternal to the computer 402.

The computer 402 also includes a memory 407 that can hold data for thecomputer 402 or a combination of components connected to the network 430(whether illustrated or not). Memory 407 can store any data consistentwith the present disclosure. In some implementations, memory 407 can bea combination of two or more different types of memory (for example, acombination of semiconductor and magnetic storage) according toparticular needs, desires, or particular implementations of the computer402 and the described functionality. Although illustrated as a singlememory 407 in FIG. 4, two or more memories 407 (of the same, different,or combination of types) can be used according to particular needs,desires, or particular implementations of the computer 402 and thedescribed functionality. While memory 407 is illustrated as an internalcomponent of the computer 402, in alternative implementations, memory407 can be external to the computer 402.

The application 408 can be an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 402 and the described functionality. Forexample, application 408 can serve as one or more components, modules,or applications. Further, although illustrated as a single application408, the application 408 can be implemented as multiple applications 408on the computer 402. In addition, although illustrated as internal tothe computer 402, in alternative implementations, the application 408can be external to the computer 402.

The computer 402 can also include a power supply 414. The power supply414 can include a rechargeable or non-rechargeable battery that can beconfigured to be either user- or non-user-replaceable. In someimplementations, the power supply 414 can include power-conversion andmanagement circuits, including recharging, standby, and power managementfunctionalities. In some implementations, the power-supply 414 caninclude a power plug to allow the computer 402 to be plugged into a wallsocket or a power source to, for example, power the computer 402 orrecharge a rechargeable battery.

There can be any number of computers 402 associated with, or externalto, a computer system containing computer 402, with each computer 402communicating over network 430. Further, the terms client, user, andother appropriate terminology can be used interchangeably, asappropriate, without departing from the scope of the present disclosure.Moreover, the present disclosure contemplates that many users can useone computer 402 and one user can use multiple computers 402.

FIG. 5 illustrates a process for generation of a reservoir digital twin(for example, the virtual 3D model 112 illustrated in FIG. 1). In someimplementations the process of FIG. 5 is performed by the computersystem illustrated and described in more detail with reference to FIG.4.

The computer system generates 504 the virtual 3D model 112 of ahydrocarbon reservoir using a machine learning algorithm. The machinelearning algorithm is trained using information obtained from multiplehydrocarbon wells. The virtual 3D model 112 includes a reservoirpressure model 132 of the hydrocarbon reservoir indicating variations inreservoir pressure 114 in accordance with time. The virtual 3D model 112further includes a fluid saturation model 134 of the hydrocarbonreservoir indicating variations in reservoir saturation 136 inaccordance with time.

The computer system executes 508 the machine learning algorithm todetermine the variations in the reservoir pressure 114 and thevariations in the reservoir saturation 136 with respect to the multiplehydrocarbon wells based on the virtual 3D model 112.

A display device (for example, the display device 424 in FIG. 4) of thecomputer system generates 512 a graphical representation of thevariations in the reservoir permeability 138, the variations in thereservoir pressure 114, and the variations in the reservoir saturation136 in accordance with time. The display device 424 is illustrated anddescribed in more detail with reference to FIG. 4. The graphicalrepresentation portrays the variations in the reservoir permeability138, the variations in the reservoir pressure 114, and the variations inthe reservoir saturation 136 in accordance with time. The graphicalrepresentation can include text, pie charts, bar graphs, and numericalvalues.

What is claimed is:
 1. A method comprising: generating, by a computersystem, a virtual three-dimensional (3D) model of a hydrocarbonreservoir using a machine learning algorithm, the machine learningalgorithm trained using information obtained from a plurality ofhydrocarbon wells, the virtual 3D model comprising: a reservoir pressuremodel of the hydrocarbon reservoir indicating variations in reservoirpressure in accordance with time; and a fluid saturation model of thehydrocarbon reservoir indicating variations in reservoir saturation inaccordance with time; executing, by the computer system, the machinelearning algorithm to determine the variations in the reservoir pressureand the variations in the reservoir saturation with respect to theplurality of hydrocarbon wells based on the virtual 3D model; andgenerating, by a display device of the computer system, a graphicalrepresentation of the variations in the reservoir pressure and thevariations in the reservoir saturation in accordance with time.
 2. Themethod of claim 1, wherein the virtual 3D model further comprises adynamic permeability model of the hydrocarbon reservoir indicatingvariations in reservoir permeability in accordance with a depth from asurface of the Earth.
 3. The method of claim 1, wherein the generatingof the virtual 3D model comprises generating, by the machine learningalgorithm, the reservoir pressure model, and wherein the hydrocarbonreservoir comprises a plurality of formations.
 4. The method of claim 3,wherein the reservoir pressure model indicates a vertical reservoirpressure gradient across the plurality of formations.
 5. The method ofclaim 1, further comprising transforming, by the computer system, theinformation obtained from the plurality of hydrocarbon wells using avariogram in accordance with the variations in the reservoir saturation.6. The method of claim 1, further comprising determining, by thecomputer system, locations of infill drillings and a logging frequencybased on the virtual 3D model.
 7. The method of claim 6, wherein thelocations of the infill drilling and the logging frequency areassociated with an increase in a sweep efficiency of the hydrocarbonreservoir.
 8. A non-transitory computer-readable storage medium storinginstructions executable by one or more computer processors, theinstructions when executed by the one or more computer processors causethe one or more computer processors to: generate a virtualthree-dimensional (3D) model of a hydrocarbon reservoir using a machinelearning algorithm, the machine learning algorithm trained usinginformation obtained from a plurality of hydrocarbon wells, the virtual3D model comprising: a reservoir pressure model of the hydrocarbonreservoir indicating variations in reservoir pressure in accordance withtime; and a fluid saturation model of the hydrocarbon reservoirindicating variations in reservoir saturation in accordance with time;execute the machine learning algorithm to determine the variations inthe reservoir pressure and the variations in the reservoir saturationwith respect to the plurality of hydrocarbon wells based on the virtual3D model; and generate, by a display device of the computer system, agraphical representation of the variations in the reservoir pressure andthe variations in the reservoir saturation in accordance with time. 9.The non-transitory computer-readable storage medium of claim 8, whereinthe virtual 3D model further comprises a dynamic permeability model ofthe hydrocarbon reservoir indicating variations in reservoirpermeability in accordance with a depth from a surface of the Earth. 10.The non-transitory computer-readable storage medium of claim 8, whereinthe generating of the virtual 3D model comprises generating, by themachine learning algorithm, the reservoir pressure model, and whereinthe hydrocarbon reservoir comprises a plurality of formations.
 11. Thenon-transitory computer-readable storage medium of claim 10, wherein thereservoir pressure model indicates a vertical reservoir pressuregradient across the plurality of formations.
 12. The non-transitorycomputer-readable storage medium of claim 8, wherein the instructionsfurther cause the one or more computer processors to transform theinformation obtained from the plurality of hydrocarbon wells using avariogram in accordance with the variations in the reservoir saturation.13. The non-transitory computer-readable storage medium of claim 8,wherein the instructions further cause the one or more computerprocessors to determine locations of infill drillings and a loggingfrequency based on the virtual 3D model.
 14. The non-transitorycomputer-readable storage medium of claim 13, wherein the locations ofthe infill drilling and the logging frequency are associated with anincrease in a sweep efficiency of the hydrocarbon reservoir.
 15. Acomputer system comprising: one or more computer processors; and anon-transitory computer-readable storage medium storing instructionsexecutable by the one or more computer processors, the instructions whenexecuted by the one or more computer processors cause the one or morecomputer processors to: generate a virtual three-dimensional (3D) modelof a hydrocarbon reservoir using a machine learning algorithm, themachine learning algorithm trained using information obtained from aplurality of hydrocarbon wells, the virtual 3D model comprising: areservoir pressure model of the hydrocarbon reservoir indicatingvariations in reservoir pressure in accordance with time; and a fluidsaturation model of the hydrocarbon reservoir indicating variations inreservoir saturation in accordance with time; execute the machinelearning algorithm to determine the variations in the reservoir pressureand the variations in the reservoir saturation with respect to theplurality of hydrocarbon wells based on the virtual 3D model; andgenerate, by a display device of the computer system, a graphicalrepresentation of the variations in the reservoir pressure and thevariations in the reservoir saturation in accordance with time.
 16. Thecomputer system of claim 15, wherein the virtual 3D model furthercomprises a dynamic permeability model of the hydrocarbon reservoirindicating variations in reservoir permeability in accordance with adepth from a surface of the Earth.
 17. The computer system of claim 15,wherein the generating of the virtual 3D model comprises generating, bythe machine learning algorithm, the reservoir pressure model, andwherein the hydrocarbon reservoir comprises a plurality of formations.18. The computer system of claim 17, wherein the reservoir pressuremodel indicates a vertical reservoir pressure gradient across theplurality of formations.
 19. The computer system of claim 15, whereinthe instructions further cause the one or more computer processors totransform the information obtained from the plurality of hydrocarbonwells using a variogram in accordance with the variations in thereservoir saturation.
 20. The computer system of claim 15, wherein theinstructions further cause the one or more computer processors todetermine locations of infill drillings and a logging frequency based onthe virtual 3D model.